An application of principal component analysis and logistic regression to facilitate production scheduling decision support system: an automotive industry case
Year: 2013
Type: article
Abstract: Production planning and control (PPC) systems have to deal with rising complexity and dynamics. The complexity of planning tasks is due to some existing multiple variables and dynamic factors derived from uncertainties surrounding the PPC. Although literatures on exact scheduling algorithms, simulation approaches, and heuristic methods are extensive in production planning, they seem to be inefficient because of daily fluctuations in real factories. Decision support systems can provide productive tools for production planners to offer a feasible and prompt decision in effective and robust production planning. In this paper, we propose a robust decision support tool for detailed production planning based on statistical multivariate method including principal component analysis and logistic regression. The proposed approach has been used in a real case in Iranian automotive industry. In the presence of existing multisource uncertainties, the results of applying the proposed method in the selected case show that the accuracy of daily production planning increases in comparison with the existing method.
Authors: Saeed Mehrjoo, Mahdi Bashiri
Institutions: Payame Noor University, Shahed University
Language: English
Subfield: Strategy and Management
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W2110823965